One of the paradoxes of modern genetics is the contrast between the tremendous technological advances in sequencing and genotyping during the past decade and the slow progress in identifying genes for complex diseases. These diseases involve subtle disruptions of biochemical and developmental pathways and display substantial genetic heterogeneity and gene-gene and gene-environmental interactions. It is now evident that studies will have to increase dramatically in scale. Much larger patient populations must be examined with much greater intensity, and mouse studies must go hand in hand with human studies. To handle the massive increases in data flow and extract the maximum amount of information from it, better statistical analysis tools must be made available to the human genetics community. The current grant supports construction of new statistical methods and their translation into user-friendly software via the widely used programs Mendel and SimWalk. Under the auspices of the grant, we will tackle a series of related projects on haplotyping, linkage mapping, disease-marker association testing, and inbred-strain mapping. These advances will free statistical analysis from restrictive assumptions, deal with multivariate traits in a unified manner, and enable understanding of genetic interactions and causality.